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Data Science Weekly Newsletter
May 16, 2019

Editor's Picks

  • How Noisy Is Your Neighborhood? Now There's A Map For That
    There's no denying it: Los Angeles isn't exactly gentle on the ears. That's one lesson, at least, from a comprehensive noise map created by the U.S. Bureau of Transportation Statistics. On the interactive U.S. map the agency released this week, which depicts data on noise produced primarily by airports and interstate highways, few spots glare with such deep and angry color as the City of Angels. Blame the area's handful of major airports and its legendary snarls of traffic — ranked this year as the worst in the nation...
  • The Empty Promise of Data Moats
    Data has long been lauded as a competitive moat for companies, and that narrative’s been further hyped with the recent wave of AI startups. Network effects have been similarly promoted as a defensible force in building software businesses. So of course, we constantly hear about the combination of the two: “data network effects” (heck, we’ve talked about them at length ourselves). But for enterprise startups — which is where we focus — we now wonder if there’s practical evidence of data network effects at all...
  • Swedish Distillery Creates First Whisky Designed By AI
    Would you drink a whisky designed and created by artificial intelligence? This fall, this hypothetical question becomes a reality, as popular award-winning Swedish whisky distillery Mackmyra releases the first ever whisky, a single malt, designed with machine learning...

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Data Science Articles & Videos

  • This neural net would like to deliver these petitions
    So the other day I heard from, a company that lets anyone make an online petition and gather signatures. In over a decade of existence, they’ve hosted about 5 million unique petitions. Some of the petitions are VERY unique - like the ongoing petition to sell Montana to Canada, which gathered so many signatures that the Montana House of Representatives introduced a bill to release a statement opposing the sale. The bill failed to pass. The question that - and I - became obsessed with is: if I trained a neural network on the full list of petitions, what kinds of demands would it generate?...
  • TensorFlow Model Optimization Toolkit — Pruning API
    Since we introduced the Model Optimization Toolkit — a suite of techniques that developers, both novice and advanced, can use to optimize machine learning models — we have been busy working on our roadmap to add several new approaches and tools. Today, we are happy to share the new weight pruning API...
  • Introducing Translatotron: End-to-End Speech-to-Speech Translation Model
    Translatotron is based on a sequence-to-sequence network which takes source spectrograms as input and generates spectrograms of the translated content in the target language. It also makes use of two other separately trained components: a neural vocoder that converts output spectrograms to time-domain waveforms, and, optionally, a speaker encoder that can be used to maintain the character of the source speaker’s voice in the synthesized translated speech...
  • Will Smith Was Wrong About the Robots
    I, Robot was first released to theaters back in 2004. In it, the movie’s filmmakers paint a fictional future (2035) where humanoid robots serve humanities needs. Fast-forward to 2019, not even two decades after the film came out, and “robots” can perform complex, multifaceted tasks with jaw-dropping results...
  • Learning 3D Human Dynamics from Video
    We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features...
  • A Survey Of The European Union's AI Ecosystem
    Compared to other global powers, the European Union (EU) is rarely considered a leading player in the development of artificial intelligence (AI). Why is this, and does this in fact accurately reflect the EU’s activities related to AI? What would it take for the EU to take a more leading role in AI, and to be internationally recognised as such?...
  • COCO-GAN: Generation by Parts via Conditional Coordinating
    Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observations to piece together the surrounding environment. Inspired by such behavior and the fact that machines also have computational constraints, we propose \underline{CO}nditional \underline{CO}ordinate GAN (COCO-GAN) of which the generator generates images by parts based on their spatial coordinates as the condition. On the other hand, the discriminator learns to justify realism across multiple assembled patches by global coherence, local appearance, and edge-crossing continuity...
  • Fathoming the Deep in Deep Learning – A Practical Approach
    Deep in ‘Deep Learning’ is elusive yet approachable with a bit of mathematics. This beckons a practical question: Is elementary calculus sufficient to unravel deep learning? The answer is yes indeed, armed with an unbound curiosity to learn and re-learn new and old alike and possibly if you can methodically follow each section, I reckon you’ll cross the chasm to intuitively understand and apply every concepts including calculus in their glory to de-clutter all intricacies of deep learning...


Big Data and AI Toronto 2019

Big Data and AI Toronto is a 2-in-1 learning experience engineered to address the greatest business challenges technology leaders are facing today.
During 2 days of case studies, demos and panels, attendees will engage with global thought-leaders in Big Data and AI, including experts from Uber, Bloomberg and SAS!
Register for your free expo pass and join 5000 attendees, 150 speakers, and 90 exhibiting brands on June 12-13 th at The Metro Toronto Convention Centre.
Stay up-to-date on the newest speakers and program highlights by subscribing to the Big Data and AI Toronto  newsletter

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  • Data Enginner / Data Scientist - Validate Health - Chicago

    Interested in being part of a small founding team, so you can see your direct impact on improving the healthcare industry? Want to be one of the rockstars building an innovative product from the ground up?​
    Validate Health is an early stage healthcare analytics company on a mission to improve accessibility to healthcare by enabling medical organizations to operate at stable and sustainable financial models.
    This position is a versatile combination of Data Engineer and Data Scientist roles. You’ll get to play a key role in shaping the delivery of powerful data-driven products that enable sustainable value-based healthcare models...

Training & Resources

  • The ultimate guide to Google Sheets as a reliable data source
    This is a tricky problem for a data engineer — my colleagues don’t have the technical skill to interact directly with our data stack, and I don’t want to have to support my own web form or similarly involved infrastructure to collect this information. What if we could sync their Google Sheet directly to a table in our data warehouse?...
  • Linux for Data Scientists, Part 1
    Because of my background, some of my mathematically-inclined colleagues have solicited my advice on how to become more adept with computers. Personally, I believe that acquiring a base level of computer knowledge can make a data scientist several times more productive (especially on routine projects). This efficiency is gained primarily in two ways: coming to understand the Linux environment in which you work, and becoming skilled at manipulating that environment...


  • Reproducible Research with R and R Studio
    "a very practical book that teaches good practice in organizing reproducible data analysis and comes with a series of examples..."...
    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page

    P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian

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